| <div align ="center"> | |
| <img src="./assets/logo.jpeg" width="20%"> | |
| <h1> ControlAR </h1> | |
| <h3> Controllable Image Generation with Autoregressive Models </h3> | |
| Zongming Li<sup>1,\*</sup>, [Tianheng Cheng](https://scholar.google.com/citations?user=PH8rJHYAAAAJ&hl=zh-CN)<sup>1,\*</sup>, [Shoufa Chen](https://shoufachen.com/)<sup>2</sup>, [Peize Sun](https://peizesun.github.io/)<sup>2</sup>, Haocheng Shen<sup>3</sup>,Longjin Ran<sup>3</sup>, Xiaoxin Chen<sup>3</sup>, [Wenyu Liu](http://eic.hust.edu.cn/professor/liuwenyu)<sup>1</sup>, [Xinggang Wang](https://xwcv.github.io/)<sup>1,📧</sup> | |
| <sup>1</sup> Huazhong University of Science and Technology, | |
| <sup>2</sup> The University of Hong Kong | |
| <sup>3</sup> vivo AI Lab | |
| <b>ICLR 2025</b> | |
| (\* equal contribution, 📧 corresponding author) | |
| [](https://arxiv.org/abs/2410.02705) | |
| [](https://huggingface.co/spaces/wondervictor/ControlAR) | |
| [](https://huggingface.co/wondervictor/ControlAR) | |
| </div> | |
| <div align="center"> | |
| <img src="./assets/vis.png"> | |
| </div> | |
| ## News | |
| `[2025-01-23]:` Our ControlAR has been accepted by ICLR 2025 🚀 !\ | |
| `[2024-12-12]:` We introduce a control strength factor, employ a larger control encoder(dinov2-base), and optimize text alignment capabilities along with generation diversity. New model weight: depth_base.safetensors and edge_base.safetensors. The edge_base.safetensors can handle three types of edges, including Canny, HED, and Lineart.\ | |
| `[2024-10-31]:` The code and models have been released!\ | |
| `[2024-10-04]:` We have released the [technical report of ControlAR](https://arxiv.org/abs/2410.02705). Code, models, and demos are coming soon! | |
| ## Highlights | |
| * ControlAR explores an effective yet simple *conditional decoding* strategy for adding spatial controls to autoregressive models, e.g., [LlamaGen](https://github.com/FoundationVision/LlamaGen), from a sequence perspective. | |
| * ControlAR supports *arbitrary-resolution* image generation with autoregressive models without hand-crafted special tokens or resolution-aware prompts. | |
| ## TODO | |
| - [x] release code & models. | |
| - [x] release demo code and HuggingFace demo: [HuggingFace Spaces 🤗](https://huggingface.co/spaces/wondervictor/ControlAR) | |
| ## Results | |
| We provide both quantitative and qualitative comparisons with diffusion-based methods in the technical report! | |
| <div align="center"> | |
| <img src="./assets/comparison.png"> | |
| </div> | |
| ## Models | |
| We released checkpoints of text-to-image ControlAR on different controls and settings, *i.e.* arbitrary-resolution generation. | |
| | AR Model | Type | Control encoder | Control | Arbitrary-Resolution | Checkpoint | | |
| | :--------| :--: | :-------------: | :-----: | :------------------: | :--------: | | |
| | [LlamaGen-XL](https://github.com/FoundationVision/LlamaGen#-text-conditional-image-generation) | t2i | DINOv2-small | Canny Edge | ✅ | [ckpt](https://huggingface.co/wondervictor/ControlAR/blob/main/canny_MR.safetensors) | | |
| | [LlamaGen-XL](https://github.com/FoundationVision/LlamaGen#-text-conditional-image-generation) | t2i | DINOv2-small | Depth | ✅ | [ckpt](https://huggingface.co/wondervictor/ControlAR/blob/main/depth_MR.safetensors) | | |
| | [LlamaGen-XL](https://github.com/FoundationVision/LlamaGen#-text-conditional-image-generation) | t2i | DINOv2-small | HED Edge | ❌ | [ckpt](https://huggingface.co/wondervictor/ControlAR/blob/main/hed.safetensors) | | |
| | [LlamaGen-XL](https://github.com/FoundationVision/LlamaGen#-text-conditional-image-generation) | t2i | DINOv2-small | Seg. Mask | ❌ | [ckpt](https://huggingface.co/wondervictor/ControlAR/blob/main/seg_cocostuff.safetensors) | | |
| | [LlamaGen-XL](https://github.com/FoundationVision/LlamaGen#-text-conditional-image-generation) | t2i | DINOv2-base | Edge (Canny, Hed, Lineart) | ❌ | [ckpt](https://huggingface.co/wondervictor/ControlAR/blob/main/edge_base.safetensors) | | |
| | [LlamaGen-XL](https://github.com/FoundationVision/LlamaGen#-text-conditional-image-generation) | t2i | DINOv2-base | Depth | ❌ | [ckpt](https://huggingface.co/wondervictor/ControlAR/blob/main/depth_base.safetensors) | | |
| ## Getting Started | |
| ### Installation | |
| ```bash | |
| conda create -n ControlAR python=3.10 | |
| git clone https://github.com/hustvl/ControlAR.git | |
| cd ControlAR | |
| pip install torch==2.1.2+cu118 --extra-index-url https://download.pytorch.org/whl/cu118 | |
| pip install -r requirements.txt | |
| pip3 install -U openmim | |
| mim install mmengine | |
| mim install "mmcv==2.1.0" | |
| pip3 install "mmsegmentation>=1.0.0" | |
| pip3 install mmdet | |
| git clone https://github.com/open-mmlab/mmsegmentation.git | |
| ``` | |
| ### Pretrained Checkpoints for ControlAR | |
| |tokenizer| text encoder |LlamaGen-B|LlamaGen-L|LlamaGen-XL| | |
| |:-------:|:------------:|:--------:|:--------:|:---------:| | |
| |[vq_ds16_t2i.pt](https://huggingface.co/peizesun/llamagen_t2i/resolve/main/vq_ds16_t2i.pt)|[flan-t5-xl](https://huggingface.co/google/flan-t5-xl)|[c2i_B_256.pt](https://huggingface.co/FoundationVision/LlamaGen/resolve/main/c2i_B_256.pt)|[c2i_L_256.pt](https://huggingface.co/FoundationVision/LlamaGen/resolve/main/c2i_L_256.pt)|[t2i_XL_512.pt](https://huggingface.co/peizesun/llamagen_t2i/resolve/main/t2i_XL_stage2_512.pt)| | |
| We recommend storing them in the following structures: | |
| ``` | |
| |---checkpoints | |
| |---t2i | |
| |---canny/canny_MR.safetensors | |
| |---hed/hed.safetensors | |
| |---depth/depth_MR.safetensors | |
| |---seg/seg_cocostuff.safetensors | |
| |---edge_base.safetensors | |
| |---depth_base.safetensors | |
| |---t5-ckpt | |
| |---flan-t5-xl | |
| |---config.json | |
| |---pytorch_model-00001-of-00002.bin | |
| |---pytorch_model-00002-of-00002.bin | |
| |---pytorch_model.bin.index.json | |
| |---tokenizer.json | |
| |---vq | |
| |---vq_ds16_c2i.pt | |
| |---vq_ds16_t2i.pt | |
| |---llamagen (Only necessary for training) | |
| |---c2i_B_256.pt | |
| |---c2i_L_256.pt | |
| |---t2i_XL_stage2_512.pt | |
| ``` | |
| ### Demo | |
| Coming soon... | |
| ### Sample & Generation | |
| #### 1. Class-to-image genetation | |
| ```bash | |
| python autoregressive/sample/sample_c2i.py \ | |
| --vq-ckpt checkpoints/vq/vq_ds16_c2i.pt \ | |
| --gpt-ckpt checkpoints/c2i/canny/LlamaGen-L.pt \ | |
| --gpt-model GPT-L --seed 0 --condition-type canny | |
| ``` | |
| #### 2. Text-to-image generation | |
| *Generate an image using HED edge and text-to-image ControlAR:* | |
| ```bash | |
| python autoregressive/sample/sample_t2i.py \ | |
| --vq-ckpt checkpoints/vq/vq_ds16_t2i.pt \ | |
| --gpt-ckpt checkpoints/t2i/hed/hed.safetensors \ | |
| --gpt-model GPT-XL --image-size 512 \ | |
| --condition-type hed --seed 0 --condition-path condition/example/t2i/multigen/eye.png | |
| ``` | |
| *Generate an image using segmentation mask and text-to-image ControlAR:* | |
| ```bash | |
| python autoregressive/sample/sample_t2i.py \ | |
| --vq-ckpt checkpoints/vq/vq_ds16_t2i.pt \ | |
| --gpt-ckpt checkpoints/t2i/seg/seg_cocostuff.safetensors \ | |
| --gpt-model GPT-XL --image-size 512 \ | |
| --condition-type seg --seed 0 --condition-path condition/example/t2i/cocostuff/doll.png \ | |
| --prompt 'A stuffed animal wearing a mask and a leash, sitting on a pink blanket' | |
| ``` | |
| #### 3. Text-to-image generation with adjustable control strength | |
| *Generate an image using depth map and text-to-image ControlAR:* | |
| ```bash | |
| python autoregressive/sample/sample_t2i.py \ | |
| --vq-ckpt checkpoints/vq/vq_ds16_t2i.pt \ | |
| --gpt-ckpt checkpoints/t2i/depth_base.safetensors \ | |
| --gpt-model GPT-XL --image-size 512 \ | |
| --condition-type seg --seed 0 --condition-path condition/example/t2i/multigen/bird.jpg \ | |
| --prompt 'A bird made of blue crystal' \ | |
| --adapter-size base \ | |
| --control-strength 0.6 | |
| ``` | |
| *Generate an image using lineart edge and text-to-image ControlAR:* | |
| ```bash | |
| python autoregressive/sample/sample_t2i.py \ | |
| --vq-ckpt checkpoints/vq/vq_ds16_t2i.pt \ | |
| --gpt-ckpt checkpoints/t2i/edge_base.safetensors \ | |
| --gpt-model GPT-XL --image-size 512 \ | |
| --condition-type lineart --seed 0 --condition-path condition/example/t2i/multigen/girl.jpg \ | |
| --prompt 'A girl with blue hair' \ | |
| --adapter-size base \ | |
| --control-strength 0.6 | |
| ``` | |
| (you can change lineart to canny_base or hed) | |
| #### 4. Arbitrary-resolution generation | |
| ```bash | |
| python3 autoregressive/sample/sample_t2i_MR.py --vq-ckpt checkpoints/vq/vq_ds16_t2i.pt \ | |
| --gpt-ckpt checkpoints/t2i/depth_MR.safetensors --gpt-model GPT-XL --image-size 768 \ | |
| --condition-type depth --condition-path condition/example/t2i/multi_resolution/bird.jpg \ | |
| --prompt 'colorful bird' --seed 0 | |
| ``` | |
| ```bash | |
| python3 autoregressive/sample/sample_t2i_MR.py --vq-ckpt checkpoints/vq/vq_ds16_t2i.pt \ | |
| --gpt-ckpt checkpoints/t2i/canny_MR.safetensors --gpt-model GPT-XL --image-size 768 \ | |
| --condition-type canny --condition-path condition/example/t2i/multi_resolution/bird.jpg \ | |
| --prompt 'colorful bird' --seed 0 | |
| ``` | |
| ### Preparing Datasets | |
| We provide the dataset datails for evaluation and training. If you don't want to train ControlAR, just download the validation splits. | |
| #### 1. Class-to-image | |
| * Download [ImageNet](https://image-net.org/) and save it to `data/imagenet/data`. | |
| #### 2. Text-to-image | |
| * Download [ADE20K with caption](https://huggingface.co/datasets/limingcv/Captioned_ADE20K)(~7GB) and save the `.parquet` files to `data/Captioned_ADE20K/data`. | |
| * Download [COCOStuff with caption](https://huggingface.co/datasets/limingcv/Captioned_COCOStuff)( ~62GB) and save the .parquet files to `data/Captioned_COCOStuff/data`. | |
| * Download [MultiGen-20M](https://huggingface.co/datasets/limingcv/MultiGen-20M_depth)( ~1.22TB) and save the .parquet files to `data/MultiGen20M/data`. | |
| #### 3. Preprocessing datasets | |
| To save training time, we adopt the tokenizer to pre-process the images with the text prompts. | |
| * ImageNet | |
| ```bash | |
| bash scripts/autoregressive/extract_file_imagenet.sh \ | |
| --vq-ckpt checkpoints/vq/vq_ds16_c2i.pt \ | |
| --data-path data/imagenet/data/val \ | |
| --code-path data/imagenet/val/imagenet_code_c2i_flip_ten_crop \ | |
| --ten-crop --crop-range 1.1 --image-size 256 | |
| ``` | |
| * ADE20k | |
| ```sh | |
| bash scripts/autoregressive/extract_file_ade.sh \ | |
| --vq-ckpt checkpoints/vq/vq_ds16_t2i.pt \ | |
| --data-path data/Captioned_ADE20K/data --code-path data/Captioned_ADE20K/val \ | |
| --ten-crop --crop-range 1.1 --image-size 512 --split validation | |
| ``` | |
| * COCOStuff | |
| ```bash | |
| bash scripts/autoregressive/extract_file_cocostuff.sh \ | |
| --vq-ckpt checkpoints/vq/vq_ds16_t2i.pt \ | |
| --data-path data/Captioned_COCOStuff/data --code-path data/Captioned_COCOStuff/val \ | |
| --ten-crop --crop-range 1.1 --image-size 512 --split validation | |
| ``` | |
| * MultiGen | |
| ```bash | |
| bash scripts/autoregressive/extract_file_multigen.sh \ | |
| --vq-ckpt checkpoints/vq/vq_ds16_t2i.pt \ | |
| --data-path data/MultiGen20M/data --code-path data/MultiGen20M/val \ | |
| --ten-crop --crop-range 1.1 --image-size 512 --split validation | |
| ``` | |
| ### Testing and Evaluation | |
| #### 1. Class-to-image generation on ImageNet | |
| ```bash | |
| bash scripts/autoregressive/test_c2i.sh \ | |
| --vq-ckpt ./checkpoints/vq/vq_ds16_c2i.pt \ | |
| --gpt-ckpt ./checkpoints/c2i/canny/LlamaGen-L.pt \ | |
| --code-path /path/imagenet/val/imagenet_code_c2i_flip_ten_crop \ | |
| --gpt-model GPT-L --condition-type canny --get-condition-img True \ | |
| --sample-dir ./sample --save-image True | |
| ``` | |
| ```bash | |
| python create_npz.py --generated-images ./sample/imagenet/canny | |
| ``` | |
| Then download imagenet [validation data](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/256/VIRTUAL_imagenet256_labeled.npz) which contains 10000 images, or you can use the whole validation data as reference data by running [val.sh](scripts/tokenizer/val.sh). | |
| Calculate the FID score: | |
| ```bash | |
| python evaluations/c2i/evaluator.py /path/imagenet/val/FID/VIRTUAL_imagenet256_labeled.npz \ | |
| sample/imagenet/canny.npz | |
| ``` | |
| #### 2. Text-to-image generation on ADE20k | |
| Download Mask2Former([weight](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221203_235933-7120c214.pth)) and save it to `evaluations/`. | |
| Use this command to get 2000 images based on the segmentation mask: | |
| ```bash | |
| bash scripts/autoregressive/test_t2i.sh --vq-ckpt checkpoints/vq/vq_ds16_t2i.pt \ | |
| --gpt-ckpt checkpoints/t2i/seg/seg_ade20k.pt \ | |
| --code-path data/Captioned_ADE20K/val --gpt-model GPT-XL --image-size 512 \ | |
| --sample-dir sample/ade20k --condition-type seg --seed 0 | |
| ``` | |
| Calculate mIoU of the segmentation masks from the generated images: | |
| ```sh | |
| python evaluations/ade20k_mIoU.py | |
| ``` | |
| #### 3. Text-to-image generation on COCOStuff | |
| Download DeepLabV3([weight](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k_20210709_155402-3cbca14d.pth)) and save it to `evaluations/`. | |
| Generate images using segmentation masks as condition controls: | |
| ```bash | |
| bash scripts/autoregressive/test_t2i.sh --vq-ckpt checkpoints/vq/vq_ds16_t2i.pt \ | |
| --gpt-ckpt checkpoints/t2i/seg/seg_cocostuff.pt \ | |
| --code-path data/Captioned_COCOStuff/val --gpt-model GPT-XL --image-size 512 \ | |
| --sample-dir sample/cocostuff --condition-type seg --seed 0 | |
| ``` | |
| Calculate mIoU of the segmentation masks from the generated images: | |
| ```bash | |
| python evaluations/cocostuff_mIoU.py | |
| ``` | |
| #### 4. Text-to-image generation on MultiGen-20M | |
| We adopt **generation with HED edges** as the example: | |
| Generate 5000 images based on the HED edges generated from validation images | |
| ```sh | |
| bash scripts/autoregressive/test_t2i.sh --vq-ckpt checkpoints/vq/vq_ds16_t2i.pt \ | |
| --gpt-ckpt checkpoints/t2i/hed/hed.safetensors --code-path data/MultiGen20M/val \ | |
| --gpt-model GPT-XL --image-size 512 --sample-dir sample/multigen/hed \ | |
| --condition-type hed --seed 0 | |
| ``` | |
| Evaluate the conditional consistency (SSIM): | |
| ```bash | |
| python evaluations/hed_ssim.py | |
| ``` | |
| Calculate the FID score: | |
| ```bash | |
| python evaluations/clean_fid.py --val-images data/MultiGen20M/val/image --generated-images sample/multigen/hed/visualization | |
| ``` | |
| ### Training ControlAR | |
| #### 1. Class-to-image (Canny) | |
| ```bash | |
| bash scripts/autoregressive/train_c2i_canny.sh --cloud-save-path output \ | |
| --code-path data/imagenet/train/imagenet_code_c2i_flip_ten_crop \ | |
| --image-size 256 --gpt-model GPT-B --gpt-ckpt checkpoints/llamagen/c2i_B_256.pt | |
| ``` | |
| #### 2. Text-to-image (Canny) | |
| ```bash | |
| bash scripts/autoregressive/train_t2i_canny.sh | |
| ``` | |
| ## Acknowledgments | |
| The development of ControlAR is based on [LlamaGen](https://github.com/FoundationVision/LlamaGen), [ControlNet](https://github.com/lllyasviel/ControlNet), [ControlNet++](https://github.com/liming-ai/ControlNet_Plus_Plus), and [AiM](https://github.com/hp-l33/AiM), and we sincerely thank the contributors for thoese great works! | |
| ## Citation | |
| If you find ControlAR is useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry. | |
| ```bibtex | |
| @article{li2024controlar, | |
| title={ControlAR: Controllable Image Generation with Autoregressive Models}, | |
| author={Zongming Li, Tianheng Cheng, Shoufa Chen, Peize Sun, Haocheng Shen, Longjin Ran, Xiaoxin Chen, Wenyu Liu, Xinggang Wang}, | |
| year={2024}, | |
| eprint={2410.02705}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV}, | |
| url={https://arxiv.org/abs/2410.02705}, | |
| } | |
| ``` | |